Data services technologies have enabled multiple computing resources to provide coordinated and distinct solutions. An example of data services, data warehouses bring together multiple systems to provide storage solutions to user needs. Data warehouses can span a vast array of computing resources. The computing resources utilized in data warehouse applications are dispersed across networks and locations. Dispersed computing resources are remotely controlled and managed. Usually, manual or scripted solutions provide installation and configuration support to data warehouse assets. Manual installation solutions by human components of widely dispersed computing resources are not cost effective. Scripted solutions are not flexible to meet dynamic requirements.
Effective access to data sets stored in data warehouses and similar data services are an area in contention for improvement among modern data services solutions. Most data consumers are not technically skilled to effectively extract data from data services. Many lack skills to write queries to provide solutions to data extraction demands. Others, while having sufficient technical skill, may lack privileges sufficient to extract data from the services. Yet others may simply lack resources and time to effectively query data sets and extract data to fulfill demand. Alternatively, application based solutions remove the need for technical skill to extract data from data services. However, most application based solutions provide standardized solutions that limit access to the data. Standardized solutions can lack fine tuning functionality to enable a user to further refine extraction solutions sufficient to fulfill user demand.